AI as a Driver of Efficiency in Waste Management and Resource Recovery
DOI:
https://doi.org/10.33050/italic.v2i2.547Keywords:
Waste Management, Resource Recovery, Artificial Intelligence, Machine Learning, Artificial Neural NetworksAbstract
Effective waste management and resource recovery are essential for maintaining environmental sustainability. With the increasing volume of waste generated from industrial and domestic activities, there is a critical need for strategies that reduce environmental impact and enhance resource utilization efficiency. This study explores the application of artificial intelligence (AI) technologies, specifically Machine Learning (ML) and Artificial Neural Networks (ANN), in optimizing waste management processes. The research demonstrates that AI can significantly improve waste classification accuracy, predict waste volumes, and identify resource recovery opportunities. Implementing AI solutions resulted in a 15% increase in resource recovery efficiency and a 20% reduction in operational costs. These findings provide valuable insights for stakeholders and policymakers in integrating AI technologies to achieve more sustainable waste management practices.
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Copyright (c) 2024 Li Wei Ming, James Anderson, Farhan Hidayat, Firdaus Dwi Yulian, Nanda Septiani (Author)
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